Objective To investigate the relationship among microvessel density (MVD),the expressions of Neu gene and estrogen receptor(ER) and prognosis in early breast cancer.Methods MVD,the expression of Neu gene and ER was examined in radical operation samples from 94 cases of breast cancer without lymphatic metastatis.Results The MVD, expression of Neu gene and ER in 94 cases of early breast cancer were 35 4?9 8,44 percent(41/94),61 percent (58/94)respectively;Three years after operation,16 cases had relapse in 94 cases of early breast cancer. The MVD of the relapse cases were 42 5?10 6, which was higher than that of non-relapse,whose MVD was 31 4?8 7,there was obvious difference(P0 05).Conclusions Whether there is a lymphoglandula metastasis is an important factor in evaluating prognosis of breast cancer.After radical operation there still are partly replase case in the breast cancer without lymphoglandula metastasis.MVD and Neu gene expression are important factors to estimate prognosis of the early breast cancer.Breast cancer which has higher MVD and Neu gene expression has worse prognosis than that of lower MVD and negative Neu gene expression. [

Antimicrobial peptides (AMPs) are small molecule peptides that are widely found in living organisms with broad-spectrum antibacterial activity and immunomodulatory effect. Due to slower emergence of resistance, excellent clinical potential and wide range of application, AMP is a strong alternative to conventional antibiotics. AMP recognition is a significant direction in the field of AMP research. The high cost, low efficiency and long period shortcomings of the wet experiment methods prevent it from meeting the need for the large-scale AMP recognition. Therefore, computer-aided identification methods are important supplements to AMP recognition approaches, and one of the key issues is how to improve the accuracy. Protein sequences could be approximated as a language composed of amino acids. Consequently, rich features may be extracted using natural language processing (NLP) techniques. In this paper, we combine the pre-trained model BERT and the fine-tuned structure Text-CNN in the field of NLP to model protein languages, develop an open-source available antimicrobial peptide recognition tool and conduct a comparison with other five published tools. The experimental results show that the optimization of the two-phase training approach brings an overall improvement in accuracy, sensitivity, specificity, and Matthew correlation coefficient, offering a novel approach for further research on AMP recognition.
Anti-Bacterial Agents/chemistry* ; Amino Acid Sequence ; Antimicrobial Cationic Peptides/chemistry* ; Antimicrobial Peptides ; Natural Language Processing